Abstract We introduce Deformable Convolution v4 (DCNv4) a highly efficient and effective
operator designed for a broad spectrum of vision applications. DCNv4 addresses the …

We study the problem of efficient semantic segmentation of large-scale 3D point clouds. By
relying on expensive sampling techniques or computationally heavy pre/post-processing …

We present a novel deep compression algorithm to reduce the memory footprint of LiDAR
point clouds. Our method exploits the sparsity and structural redundancy between points to …

We propose a local-to-global representation learning algorithm for 3D point cloud data,
which is appropriate to handle various geometric transformations, especially rotation …

Proteins perform a large variety of functions in living organisms, thus playing a key role in
biology. As of now, available learning algorithms to process protein data do not consider …

Previous point cloud semantic segmentation networks use the same process to aggregate
features from neighbors of the same category and different categories. However, the joint …

Convolutional networks are not aware of an object's geometric variations, which leads to
inefficient utilization of model and data capacity. To overcome this issue, recent works on …

Abstract Deformable Convolutional Networks (DCNs) are proposed to solve the inherent
limited geometric transformation in CNNs, showing outstanding performance on …

LiDAR is an important method for autonomous driving systems to sense the environment.
The point clouds obtained by LiDAR typically exhibit sparse and irregular distribution, thus …

Abstract 3D scanners often obtain partial point clouds due to occlusion and limitation of
viewing angles. Point cloud completion aims at inferring the full shape of an object from an …